In addition to high dimensionality, HSIC suffers from various other artifacts like high intra-class variability due to unconfined variations in reflectance values caused by several environmental interferers and degradation of data caused by instrumental noise while capturing the data [1]}. Furthermore, the addition of redundant bands due to HSI instruments affects the computational complexity of the model. Spectral mixing is another challenge related to the spatial resolution of HSI. HSI pixels with low to average spatial resolution cover vast spatial regions on the surface of earth leading to mixed spectral signatures which result in high inter-class similarity in border regions. As a result, it becomes difficult to identify the materials based on their spectral reflectance values [2]}. Following are some main challenges that come across when DL is applied to HSIC:
